Differential privacy

KAUST-CEMSE-CS-PhD-Dissertation-Defense-Lijie-Zihang Xiang-Modern-Privacy-preserving-Machine-Learning

Modern Privacy-preserving Machine Learning: Rigorous Approach for Data Privacy

Zihang Xiang, Ph.D. Student, Computer Science

Jul 6, 10:00 - 12:00

B3 L5 R5216

privacy-preserving machine learning Differential privacy Federated learning

This dissertation centers around privacy-preserving technologies (differential privacy) in broad machine learning applications. This dissertation focuses on two sides of differential privacy: 1) designing privacy-preserving algorithms, 2) ensuring the falsifiability of privacy claims.

Differentially private testing of symmetry equivalence hypotheses based on the characteristic function

Prof. Simos G. Meintanis, University of Athens

May 29, 15:30 - 16:30

B1 L4 R4102

Differential privacy Neighborhood of model validation Symmetry testing

We investigate privacy aspects of tests for symmetry equivalence null hypotheses. Specifically, we consider weighted L2 type tests as well as chi-squared type tests for multivariate symmetry based on the characteristic function, and their privacy properties are specifically quantified within the context of differential privacy. We consider both the case of known centre as well as tests for symmetry about an unknown centre.